Abstract: In the process of oil and gas exploration, development and production, reservoir pressure plays a crucial role in the accumulation, distribution and migration of oil and gas. Abnormally high-pressure reservoirs can lead to drilling accidents such as wellbore collapse, kicks and blowouts. Traditional methods for predicting reservoir pressure, mainly based on well logging calculations using empirical formula and effective stress methods, suffer from drawbacks including complex parameter identification and significant subjectivity. Consequently, the paper uses the DF block in the Yinggehai Basin as a case study, building a reservoir pressure prediction model based on real-time pressure data using both the BP neural network and convolutional neural network. This process established an implicit direct relationship between logging curves and real-time reservoir pressure, allowing for the prediction of reservoir pressure and an analysis of the causes of overpressure. The results of the study indicate that: (1) The established convolutional neural network model demonstrates high accuracy in predicting reservoir pressure, with a root mean square error of 0.27 MPa for the optimal model. (2) The predicted reservoir pressure range for the Huangliu Formation in the DF block of the Yinggehai Basin is 53.26-55.60 MPa, with an average pressure coefficient of 1.66-1.95, consistent with overpressure. (3) The mechanism behind the overpressure in the Huangliu Formation, DF block, is mainly due to fluid expansion, supplemented by undercompaction.